U.S. patent application number 16/439856 was filed with the patent office on 2020-12-17 for massive open online course assessment management.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Kuntal Dey, Sreekanth L. Kakaraparthy, Seema Nagar.
Application Number | 20200394933 16/439856 |
Document ID | / |
Family ID | 1000004143526 |
Filed Date | 2020-12-17 |
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United States Patent
Application |
20200394933 |
Kind Code |
A1 |
Nagar; Seema ; et
al. |
December 17, 2020 |
MASSIVE OPEN ONLINE COURSE ASSESSMENT MANAGEMENT
Abstract
An embodiment of the invention may include a method, computer
program product and system for transmission of data segments to a
user on a computing device. An embodiment may include controlling a
transmission of one or more assessment data segments to the user
based on a state of perception of the user determined using sensor
data for the user captured by one or more biometric sensors and
further based on profile information of the user.
Inventors: |
Nagar; Seema; (Bangalore,
IN) ; Kakaraparthy; Sreekanth L.; (Bangalore, IN)
; Dey; Kuntal; (New Delhi, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
ARMONK |
NY |
US |
|
|
Family ID: |
1000004143526 |
Appl. No.: |
16/439856 |
Filed: |
June 13, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06K 9/00302 20130101;
G09B 19/00 20130101; G09B 7/00 20130101 |
International
Class: |
G09B 19/00 20060101
G09B019/00; G06K 9/00 20060101 G06K009/00 |
Claims
1. A computer-implemented method for transmission of data segments
to a user on a computing device, the method comprising: controlling
a transmission of one or more assessment data segments to the user
based on a state of perception of the user determined using sensor
data for the user captured by one or more biometric sensors and
further based on profile information of the user.
2. The computer-implemented method of claim 1, wherein controlling
the transmission comprises: skipping at least one data segment of
the one or more assessment data segments based on the state of
perception of the user.
3. The computer-implemented method of claim 1, wherein controlling
the transmission comprises: selecting at least one data segment of
the one or more assessment data segments, from a database, for
transmission to the user, wherein the selecting is based on the
state of perception of the user; and transmitting the at least one
data segment to the user on the computing device.
4. The computer-implemented method of claim 1, wherein controlling
the transmission further comprises: determining an assessment score
based on the state of perception of the user, a determined
cognition score of the user, and a determined background match
score of the user; comparing the assessment score to a threshold
value; and transmitting at least one assessment data segment of the
one or more assessment data segments only in response to the
assessment score exceeding the threshold value.
5. The computer-implemented method of claim 1, wherein the one or
more biometric sensors comprise a camera of the computing device,
and wherein the biometric data comprise one or more facial
expressions of the user captured by the camera of the computing
device.
6. The computer-implemented method of claim 3, wherein data
segments in the database are ordered data segments, and wherein the
selecting comprises: selecting the at least one data segment out of
order based on the state of perception of the user.
7. The computer-implemented method of claim 3, wherein the at least
one data segment is selected from a group consisting of: an
assessment data segment and an educational material data
segment.
8. The computer-implemented method of claim 1, wherein controlling
a transmission comprises: receiving an ordered list of one or more
assessment data segments for transmission to the user; transmitting
at least one assessment data segment in the one or more assessment
data segments to the user; determining a state of perception of the
user using sensor data captured by one or more biometric sensors,
wherein the one or more biometric sensors comprise a camera of the
computing device; reordering at least one remaining assessment data
segment in the ordered list based on determining the state of
perception of the user; and transmitting the at least one remaining
assessment data segment.
9. The computer-implemented method of claim 1, further comprising:
presenting, by an electronic display, the one or more assessment
data segments to the user.
10. A computer program product for transmission of data segments to
a user on a computing device, the computer program product
comprising: one or more computer-readable tangible storage devices
and program instructions stored on at least one of the one or more
computer-readable tangible storage devices, wherein the program
instructions are executable by a computer, the program instructions
comprising: program instructions to control a transmission of one
or more assessment data segments to the user based on a state of
perception of the user determined using sensor data for the user
captured by one or more biometric sensors and further based on
profile information of the user.
11. The computer program product of claim 10, wherein program
instructions to control the transmission comprises: program
instructions to skip at least one data segment of the one or more
assessment data segments based on the state of perception of the
user.
12. The computer program product of claim 10, wherein program
instructions to control the transmission comprises: program
instructions to select at least one data segment of the one or more
assessment data segments, from a database, for transmission to the
user, wherein the selecting is based on the state of perception of
the user; and transmitting the at least one data segment to the
user on the computing device.
13. The computer program product of claim 10, wherein program
instructions to control the transmission further comprises: program
instructions to determine an assessment score based on the state of
perception of the user, a determined cognition score of the user,
and a determined background match score of the user; program
instructions to compare the assessment score to a threshold value;
and program instructions to transmit at least one assessment data
segment of the one or more assessment data segments only in
response to the assessment score exceeding the threshold value.
14. The computer program product of claim 10, wherein the one or
more biometric sensors comprise a camera of the computing device,
and wherein the biometric data comprise one or more facial
expressions of the user captured by the camera of the computing
device.
15. The computer program product of claim 12, wherein data segments
in the database are ordered data segments, and wherein the program
instructions to select comprises: program instructions to select
the at least one data segment out of order based on the state of
perception of the user.
16. A computer system for transmission of data segments to a user
on a computing device, the computer system comprising: one or more
processors, one or more computer-readable memories, one or more
computer-readable tangible storage devices, and program
instructions stored on at least one of the one or more
computer-readable tangible storage devices for execution by at
least one of the one or more processors via at least one of the one
or more memories, the program instructions comprising: program
instructions to control a transmission of one or more assessment
data segments to the user based on a state of perception of the
user determined using sensor data for the user captured by one or
more biometric sensors and further based on profile information of
the user.
17. The computer system of claim 16, wherein program instructions
to control the transmission comprises: program instructions to skip
at least one data segment of the one or more assessment data
segments based on the state of perception of the user.
18. The computer system of claim 16, wherein program instructions
to control the transmission comprises: program instructions to
select at least one data segment of the one or more assessment data
segments, from a database, for transmission to the user, wherein
the selecting is based on the state of perception of the user; and
transmitting the at least one data segment to the user on the
computing device.
19. The computer system of claim 16, wherein program instructions
to control the transmission further comprises: program instructions
to determine an assessment score based on the state of perception
of the user, a determined cognition score of the user, and a
determined background match score of the user; program instructions
to compare the assessment score to a threshold value; and program
instructions to transmit at least one assessment data segment of
the one or more assessment data segments only in response to the
assessment score exceeding the threshold value.
20. The computer system of claim 16, wherein the one or more
biometric sensors comprise a camera of the computing device, and
wherein the biometric data comprise one or more facial expressions
of the user captured by the camera of the computing device.
Description
BACKGROUND
[0001] Embodiments of the present invention relate generally to the
fields of online education and computer-based testing, and more
specifically, to personalized computer-administered assessments of
an individual based on the cognitive state and perception of the
individual.
[0002] In the field of online education, a massive open online
course (MOOC) is an online course aimed at unlimited participation
and open access via the internet. In addition to traditional course
materials, such as filmed lectures, readings, and problem sets,
many MOOCs provide interactive courses with user forums to support
community interactions among students, professors, and teaching
assistants. Many MOOCs also provide interactive
computer-administered assessments of an individual's progression
throughout and at the culmination of the course.
BRIEF SUMMARY
[0003] An embodiment of the invention may include a method,
computer program product and system for transmission of data
segments to a user on a computing device. An embodiment may include
controlling a transmission of one or more assessment data segments
to the user based on a state of perception of the user determined
using sensor data for the user captured by one or more biometric
sensors and further based on profile information of the user.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 is a block diagram illustrating a personalized
assessment system, in accordance with an embodiment of the present
invention;
[0005] FIG. 2 is a flowchart illustrating the operations of the
assessment engine of FIG. 1, in accordance with an embodiment of
the invention;
[0006] FIG. 3 is a flowchart illustrating further operations of the
assessment engine of FIG. 1, in accordance with an embodiment of
the invention;
[0007] FIG. 4 is a block diagram depicting the hardware components
of the personalized assessment system of FIG. 1, in accordance with
an embodiment of the invention;
[0008] FIG. 5 depicts a cloud computing environment in accordance
with an embodiment of the present invention; and
[0009] FIG. 6 depicts abstraction model layers in accordance with
an embodiment of the present invention.
DETAILED DESCRIPTION
[0010] Assessments are a key tool in education for measuring the
learning performance and course progression of an individual,
especially in online education where courses are offered through a
MOOC. Typically, while watching a video lecture provided though a
MOOC, there may be periodic assessment interruptions (e.g.,
quizzes, exams, data segments) within the lecture to assess if an
individual is focused or not, and to assess whether the individual
is understanding the course material or not. Currently, such
assessments are usually pre-designed and pre-populated data
segments. Personalization in assessments so far have focused on
attempting to assess based on the capability of an individual. For
example, if the individual is identified as a beginner, then the
assessment will focus on basic fundamentals. As another example, if
the individual did not answer a previous advanced level question
correctly, then the next question of the assessment may be an
intermediate level question. However, there lacks a means by which
to personalize the assessments, which may include ordered periodic
assessments and a final assessment, of a course beyond the afore
mentioned examples. For instance, it would be advantageous to
personalize the final assessment of a course based on the portions
of the course where the individual struggled the most as gauged
from the cognitive state of the individual at different portions of
the course and the state of perception of the individual, in
addition to profile information of the individual.
[0011] Moreover, these pre-designed and pre-populated periodic
assessment interruptions may become distractions which make it
difficult for an individual to maintain interest in the lecture.
For instance, the individual may lose interest in the lecture if
the assessment interruption addresses a part of the lecture which
was easy for the individual to understand. Rather than serving to
cement information gleaned from the lecture, such an assessment
interruption may in fact be detrimental to the flow of the lecture
and the maintained interest of the individual. As such, it would be
advantageous to implement a mechanism by which the individual can
skip potentially distracting and unnecessary assessment
interruptions (i.e., data segments) based on the cognitive state of
the individual and the state of perception of the individual, in
addition to profile information of the individual.
[0012] In an effort to meet the needs stated above, embodiments of
the present invention may include a Personalized Assessment System
(PAS) 100, described below, which presents a method for controlling
the transmission of one or more assessment data segments to an
individual, engaged in a computer-administered course (e.g., a
MOOC), based on the known cognitive state of the individual and
evolution of the state of perception (i.e., one or more perception
states) of the individual while progressing through the course, in
addition to profile information of the individual, if available.
Controlling the transmission may include skipping at least one of
the one or more assessment data segments based on the perception
state of the user. Controlling the transmission may also include
selecting at least one assessment data segment of the one or more
assessment data segments, from a database, for transmission to the
user, based on the perception state of the user and transmitting
the at least one assessment data segment to the user on the
computing device. In embodiments of the invention, PAS 100 may
include a perception-cognition-background engine (PCBE) which may
determine the cognition and perception of an individual engaged in
a computer-implemented course (e.g., a video lecture provided
through a MOOC), as well as determine background information of the
individual, if available. Based on the determined cognition,
perception, and any background information of the individual, PAS
100 may, in embodiments of the invention, provide personalized
course related computer-implemented assessment data segments (e.g.,
quiz, exam), both periodic and final, which are personalized for
the individual.
[0013] Embodiments of the present invention will now be described
in detail with reference to the accompanying Figures.
[0014] FIG. 1 is a functional block diagram illustrating
Personalized Assessment System 100, in accordance with an
embodiment of the present invention. In an example embodiment, PAS
100 may include computing device 120, profiles database 140, MOOC
database 150, perception state mapping database 160, and server
130, all interconnected via network 110.
[0015] In embodiments of the invention, personalized course related
computer-implemented assessments provided by PAS 100 may include,
for example, skipping one or more pre-designed ordered assessment
data segment interruptions in a course (e.g., a lecture video)
which were deemed too simple as judged from available profile data
of the individual, the cognitive state of the individual, and the
perception of the individual when he/she was watching that portion
of the course for which the assessment interruption is meant to
assess. In embodiments of the invention, personalized course
related computer-implemented assessments provided by PAS 100 may
further include generating a personalized final assessment data
segment at the end of the course based on which portions of the
course were more difficult for the individual as judged from
available profile data of the individual and evolution of both the
cognitive state and perception state of the individual while
progressing through the entire course. In other embodiments of the
invention, PAS 100 may also dynamically generate one or more
personalized assessment data segments at given points during the
course, prior to the end of the course, based on portions of the
course which were more difficult for the individual as judged from
available profile data of the individual, the cognitive state of
the individual, and the perception of the individual when he/she
was watching that portion of the course. In yet other embodiments
of the invention, PAS 100 may reorder at least one remaining
assessment data segment, of the ordered assessment data segment,
based on available profile data of the individual, the cognitive
state of the individual, and the perception of the individual when
he/she was watching that portion of the course for which the at
least one remaining assessment segment is meant to assess. In
various embodiments PAS 100 may transmit one or more assessment
data segments, both pre-designed ordered assessment data segments
and dynamically generated assessment data segments, to a computing
device of the user for display to the user.
[0016] In embodiments of the invention, PAS 100 may undergo an
initialization process (i.e., a bootstrapping method) whereby PAS
100 accepts and/or retrieves inputs such as, but not limited to,
various MOOC content data (e.g., video data segments, audio data
segments, graphics, text), any pre-designed and pre-populated
assessment data segments of the MOOC, a mapping which identifies
which portions of the MOOC the pre-designed and pre-populated
assessment data segments are meant to test, and any available
profile data of an individual participating in the MOOC. If profile
data of the individual participating in the MOOC is not available,
PAS 100 may assume a default level of the individual for which the
MOOC is meant to teach. During the initialization process, PAS 100
may also create a basic learner model for the individual
participating in the MOOC. In an example embodiment, the
initialization process and functionality of PAS 100 may
automatically commence in response to the individual beginning
participation in the MOOC on a computing device (e.g., computing
device 120). In another embodiment, the initialization process and
functionality of PAS 100 may commence in response to the individual
enabling PAS 100 via a button (not shown) in MOOC interface
124.
[0017] In various embodiments, network 110 is a communication
channel capable of transferring data between connected devices. In
an example embodiment, network 110 may be the Internet,
representing a worldwide collection of networks and gateways to
support communications between devices connected to the Internet.
Moreover, network 110 may include, for example, wired, wireless, or
fiber optic connections which may be implemented as an intranet
network, a local area network (LAN), a wide area network (WAN), or
any combination thereof. In further embodiments, network 110 may be
a Bluetooth network, a WiFi network, or a combination thereof. In
general, network 110 can be any combination of connections and
protocols that will support communications between computing device
120, profiles database 140, MOOC database 150, perception state
mapping database 160, and server 130.
[0018] In an example embodiment, computing device 120 may include
camera 122 and MOOC interface 124. Computing device 120 may be a
laptop computer, a notebook, a tablet computer, a netbook computer,
a personal computer (PC), a desktop computer, a personal digital
assistant (PDA), a smart phone, a wearable computing device, a
smart tv, or any other electronic device or computing system
capable of sending, and receiving data to and from other computing
devices such as profiles database 140, MOOC database 150,
perception state mapping database 160, and server 130, via network
110, and capable of supporting the functionality required of
embodiments of the invention. For example, computing device 120 may
support a communication link (e.g., wired, wireless, direct, via a
LAN, via the network 110, etc.) between computing device 120,
profiles database 140, MOOC database 150, perception state mapping
database 160, and server 130. Data sent from computing device 120
may include data from camera 122 and MOOC interface 124. Data
received by computing device 120 may include data/instructions
sent, via server 130 and network 110, from MOOC database 150 and
assessment engine 136, both described below. Computing device 120
may be described, generally, with respect to FIG. 4 below. In an
example embodiment, computing device 120 (e.g., the user's laptop)
may send data captured by camera 122 and MOOC interface 124 to
server 130, via network 110.
[0019] In an example embodiment, camera 122 may be housed within
computing device 120 and configured to provide, to MOOC interface
124, sensor data. The sensor data may include biometric data such
as digital information corresponding to captured images of a user
(e.g., still images and/or video). For example, camera 122 may be
configured to capture biometric data including one or more facial
expressions of an individual participating in an MOOC through the
use of computing device 120. It should be understood that various
types of cameras, including night vision enabled cameras, infrared
sensing cameras, etc., are within the scope of the present
invention. In various embodiments, the digital information can
correspond to, for example, a video stream, a series of images
captured at regular intervals, or images captured and transmitted
as the result of a triggering event occurring on computing device
120, such as initialization of MOOC interface 124. In various
embodiments, camera 122 may be one of multiple biometric sensors
configured to provide biometric sensor data to MOOC interface 124.
In an example embodiment, camera 122 provides digital information
corresponding to captured images (e.g., facial expressions) of an
individual participating in a MOOC to MOOC interface 124.
[0020] In an example embodiment, MOOC interface 124 may be a
program, or subroutine contained in a program, that may allow a
user of computing device 120 to interact with a MOOC module (not
shown) hosted on server 130, via network 110. For example, an
individual participating in a MOOC hosted on server 130 may watch
and/or listen to lectures and execute assessments of the MOOC via
MOOC interface 124. In addition, MOOC interface 124 may be
connectively coupled to hardware components, such as those depicted
by Figure [ ], for receiving user input, including mice, keyboards,
touchscreens, microphones, cameras, and the like. For example, MOOC
interface 124 may receive digital information from camera 122 for
transmission to server 130. In an example embodiment, MOOC
interface 124 is implemented via a web browsing application
containing a graphical user interface (GUI) and display that is
capable of transferring data files, folders, audio, video,
hyperlinks, compressed data, and other forms of data transfer
individually or in bulk. In other embodiments, MOOC interface 124
may be implemented via other integrated or standalone software
applications and hardware capable of receiving user interaction and
communicating with other electronic devices. In an example
embodiment, MOOC interface 124 may send and receive data to and
from assessment retriever 132, perception-cognition-background
engine 134, and assessment engine 136, via network 110. In
addition, MOOC interface 124 may display various types of MOOC
activities to be performed by a user of computing device 120. In an
example embodiment, MOOC interface 124 may display/present MOOC
lecture material and one or more personalized assessment data
segments at given points during the MOOC to an individual
participating in a MOOC (not shown) hosted on server 130.
Furthermore, in an example embodiment, MOOC interface 124 may send
digital information captured by camera 122 and responses to the one
or more personalized assessment data segments to server 130, via
network 110.
[0021] In an example embodiment, profiles database 140 may be a
laptop computer, tablet computer, netbook computer, personal
computer (PC), desktop computer, a networked computer appliance, or
any other networked programmable electronic device capable of
storing data and capable of an exchange of data with other
electronic devices (e.g., computing device 120 and server 130), for
example, through a network adapter, in accordance with an
embodiment of the invention. In embodiments of the present
invention, profiles database 140 may store profile data of one or
more individuals participating in a MOOC. Profile data of an
individual may include, but is not limited to, information
pertaining to prior courses taken by the individual (e.g., topic
and assessment score(s)), education level of the individual, areas
of study of the individual, and learning performance of the
individual in the current MOOC (e.g., responses to assessments
taken by the individual thus far in the current MOOC). In
embodiments of the present invention, the data stored within
profiles database 140 may be populated during an initialization
process of PAS 100. In embodiments of the present invention,
profile data of an individual within profiles database 140 may be
updated as a result of the individual's participation (i.e.,
lectures watched and/or listened to, assessments taken) in a MOOC.
In embodiments of the present invention, the data stored in
profiles database 140 may be structured (i.e. have associated
metadata), partially structured, or unstructured. Moreover, the
data within profiles database 140 may be written in programming
languages of common file formats such as .docx, .doc, .pdf, .rtf,
.mp3, .wma, .m4p, .wav, .jpg, .tif, .gif, .bmp, etc. In an example
embodiment, profiles database 140 may contain profile information
of an individual participating in a MOOC hosted by sever 130. The
profile data of the individual may be retrieved by
perception-cognition-background engine 134, via server 130 and
network 110. Profiles database 140 may be described generally with
respect to FIG. 4 below. In another embodiment, profiles database
140 may be located in server 130.
[0022] In an example embodiment, MOOC database 150 may be a laptop
computer, tablet computer, netbook computer, personal computer
(PC), desktop computer, a networked computer appliance, or any
other networked programmable electronic device capable of storing
data and capable of an exchange of data with other electronic
devices (e.g., computing device 120 and server 130), for example,
through a network adapter, in accordance with an embodiment of the
invention. In embodiments of the present invention, MOOC database
150 may store digital content of a MOOC. Digital content of a MOOC
may include, but is not limited to, audio and/or visual lectures of
the MOOC, ordered assessment data segments (e.g., quizzes, exams)
of the MOOC, and performance results (i.e., assessment scores) of
one or more individuals for each assessment taken by the one or
more individuals. MOOC database 150 may also store the
timing/scheduling of the digital content of the MOOC. Each
assessment data segment of the MOOC may be labeled with the portion
of the MOOC it is meant to assess. In embodiments of the present
invention, the data stored within MOOC database 150 may be
populated during an initialization process of PAS 100. In
embodiments of the present invention, digital content of the MOOC
within MOOC database 150 may be updated as a result of the
individual's participation (i.e., lectures watched and/or listened
to, assessments taken) in the MOOC. In embodiments of the present
invention, the data stored in MOOC database 150 may be structured
(i.e. have associated metadata), partially structured, or
unstructured. Moreover, the data within MOOC database 150 may be
written in programming languages of common file formats such as
.docx, .doc, .pdf, .rtf, .mp3, .wma, .m4p, .wav, .jpg, .tif, .gif,
.bmp, etc. In an example embodiment, MOOC database 150 may contain
digital content (e.g., lectures and assessments) of a MOOC hosted
by sever 130. In an example embodiment, the digital content of the
MOOC may be retrieved by assessment retriever 132 for transmission
to computing device 120, via server 130 and network 110. Data
within MOOC database 150 may also be accessible by
perception-cognition-background engine 134 and assessment engine
136, via server 130 and network 110. MOOC database 150 may be
described generally with respect to FIG. 4 below. In another
embodiment, MOOC database 150 may be located in server 130.
[0023] In an example embodiment, perception state mapping database
160 may be a laptop computer, tablet computer, netbook computer,
personal computer (PC), desktop computer, a networked computer
appliance, or any other networked programmable electronic device
capable of storing data and capable of an exchange of data with
other electronic devices (e.g., computing device 120 and server
130), for example, through a network adapter, in accordance with an
embodiment of the invention. In embodiments of the present
invention, perception state mapping database 160 may store one or
more perception states, as determined by PCBE 134 described below,
of one or more individuals participating in a MOOC over a period of
time. The one or more perception states of the individual over time
amount to an evolution of perception states of the individual as
he/she progresses through the MOOC. Furthermore, recording/storing
the evolution of perception states of the individual creates and
maintains a mapping of the individual's perception state to
different portions (e.g., lecture segments) of the MOOC. For
example, an individual may possess a different perception state for
different slides of a MOOC lecture. Perceptual states determined by
PCBE 134, described below, and stored within perception state
mapping database 160 may include, but are not limited to: neutral
and focused; on task and engaged concentration; confused and
concentrated on task; confused and off task; bored and attentive on
task; bored and on task; and frustrated. Additionally, perceptual
states stored within perception state mapping database 160 which
were identified, by PCBE 134, as being more difficult for the
individual may be flagged. In embodiments of the present invention,
the data stored within perception state mapping database 160 may be
populated during an individual's participation (i.e., watching
and/or listening to lectures, taking assessments) in the MOOC. In
embodiments of the present invention, the data stored in perception
state mapping database 160 may be structured (i.e. have associated
metadata), partially structured, or unstructured. Moreover, the
data within perception state mapping database 160 may be written in
programming languages of common file formats such as .docx, .doc,
.pdf, .rtf, .mp3, .wma, .m4p, .wav, .jpg, .tif, .gif, .bmp, etc. In
an example embodiment, perception state mapping database 160 may
contain one or more perception states of an individual, as
determined by PCBE engine 134 (described below), participating in a
MOOC, hosted by sever 130, which correspond to different portions
of the MOOC. In an example embodiment, the data stored within
perception state mapping database 160 may be accessed, retrieved,
and/or updated by perception-cognition-background engine 134, via
server 130 and network 110. Perception state mapping database 160
may be described generally with respect to FIG. 4 below. In another
embodiment, perception state mapping database 160 may be located in
server 130.
[0024] In an example embodiment, server 130 may include assessment
retriever 132, perception-cognition-background engine 134, and
assessment engine 136. Server 130 may be a desktop computer, a
notebook, a laptop computer, a blade server, a networked computer
appliance, a virtual device, or any other networked electronic
device or computing system capable of receiving and sending data
from and to other computing devices such as computing device 120,
profiles database 140, MOOC database 150, and perception state
mapping database 160, via network 110, and capable of supporting
the functionality required of embodiments of the invention. In an
example embodiment, server 130 may function to process data
received from computing device 120, profiles database 140, MOOC
database 150, and perception state mapping database 160, via
network 110. While server 130 is shown as a single device, in other
embodiments, server 130 may be comprised of a cluster or plurality
of computing devices, working together or working separately.
Server 130 may be described generally with respect to FIG. 4
below.
[0025] In an example embodiment, assessment retriever 132 may be a
program, or subroutine contained in a program, that may operate to
retrieve digital content of a MOOC from MOOC database 150 for
transmission to computing device 120, via server 130 and network
110. Retrieved digital content of a MOOC may include, but is not
limited to, audio and/or visual lectures of the MOOC and assessment
data segments (e.g., quizzes, exams) of the MOOC. In embodiments of
the present invention, assessment data segments of the MOOC may be
pre-designed, pre-populated, and have fixed timing within a lecture
of the MOOC. Assessment retriever 132 may record the
timing/scheduling of any pre-designed and pre-populated assessment
data segments and create one or more mappings which identify which
portion(s) of the MOOC course (e.g., audio file, video file,
presentation) the assessment data segment(s) is/are meant to
assess. In embodiments of the present invention, assessment
retriever 132 may transmit any retrieved data and any created
mappings to perception-cognition-background engine 134 and/or
assessment engine 136.
[0026] In an example embodiment, perception-cognition-background
engine (PCBE) 134 may be a program, or subroutine contained in a
program, that may operate to determine one or more states of
perception and levels of cognition of one or more individuals
participating in a MOOC. The one or more states of perception and
levels of cognition, as determined by PCBE 134, may correspond to
one or more portions of the MOOC. In an example embodiment, PCBE
134 may make these determinations in response to receiving data
from assessment retriever 132 indicating an approaching assessment
of the MOOC. PCBE 134 may also begin making these determinations
when an individual starts the MOOC and continue to make these
determinations at various points over a period of time during which
the one or more individuals are participating in the MOOC. As
mentioned above, one or more perception states of an individual
over time amount to an evolution of perception states of the
individual as he/she progresses through the MOOC. An evolution of
the individual's state of perception, as determined by PCBE 134,
may be based in part on profile information of the individual, if
available, and metrics of the individual, observed over the
duration of the MOOC, such as, but not limited to, facial
expressions, emotions, and eye gaze behavior. In embodiments,
information relating to the afore mentioned observed metrics (e.g.,
facial expressions, emotions, and eye gaze behavior) may be
gathered, in part, by camera 122 and sent to PCBE 134 via MOOC
interface 124 and network 110. Through determining one or more
perception states of the individual throughout the duration of the
MOOC, it may be feasible to identify portions of the MOOC which
were easier or more difficult for the individual. In an example
embodiment, PCBE 134 may store determined perception states of the
individual within perception state mapping database so that a
mapping of perception states corresponding to different portions of
the MOOC may be maintained. In an example embodiment, PCBE 134 may
flag determined perception states of the individual which were
identified as being more difficult for the individual. The
cognitive sate of the individual, as determined by PCBE 134, may be
based in part on the determined one or more perception states of
the individual in combination with cognition factors such as, but
not limited to, prior knowledge of the individual (e.g., individual
profile data) and prior demonstration of understanding of related
material (e.g., answers to earlier assessments in the course).
[0027] In an example embodiment, the determination of a perception
state of the individual may cause PCBE 134 to execute a cognition
retrieval process and a background retrieval process. In an example
embodiment, PCBE 134 may access data stored in profiles database
140, MOOC database 150, and perception state mapping database 160.
The cognition retrieval process, as performed by PCBE 134, may
retrieve the learning performance thus far of the individual in the
current MOOC (i.e., scores for assessments taken thus far in the
current MOOC), and compute a cognition score based on the score(s)
the individual obtained in their prior assessment(s) and the
alignment of the questions and material covered in the prior
assessment(s) with an upcoming assessment. In an example
embodiment, the computed cognition score may be sent to or
retrieved by assessment engine 136. The background retrieval
process, as performed by PCBE 134, may check for any prior
knowledge of a topic of the MOOC based on profile information
(e.g., prior courses taken in past sessions and elsewhere which are
known to PAS 100) stored in profiles database 140, and compute a
background match score based on an alignment of such prior courses
with the current MOOC material. In an example embodiment, the
computed background match score may be sent to or retrieved by
assessment engine 136.
[0028] In an example embodiment, assessment engine 136 may be a
program, or subroutine contained in a program, that may operate to
skip one or more pre-designed ordered assessment data segments
(e.g., a quiz, an exam) in a MOOC which were deemed too simple as
determined from available profile data of the individual, the
cognitive state of the individual, and the perception of the
individual when he/she was watching that portion of the MOOC for
which the assessment data segment is meant to assess. In an example
embodiment, assessment engine 136 may also operate to generate a
personalized final assessment data segment at the end of the MOOC
based on which portions of the MOOC were more difficult for the
individual as determined from available profile data of the
individual and evolution of both the cognitive state and perception
state of the individual while progressing through the entire MOOC.
Assessment engine 136 may transmit the personalized final
assessment data segment to computing device 120 for presentation to
the user via MOOC interface 124. Additionally, assessment engine
136 may operate to dynamically generate one or more personalized
assessment data segments at given points during the MOOC based on
portions of the course which were more difficult for the individual
as determined from available profile data of the individual, the
cognitive state of the individual, and the perception of the
individual when he/she was watching that portion of the MOOC. As
part of its operation, assessment engine 136 may, in an example
embodiment, access data stored within profiles database 140, MOOC
database 150, and perception state mapping database 160, via server
130 and network 110. For example, in dynamically generating the one
or more personalized assessment data segments at given points
during the MOOC, assessment engine 136 may utilize and reorder one
or more portions (e.g., questions) of any existing assessment data
segments of the MOOC stored within MOOC database 150. Assessment
engine 136 may transmit the one or more personalized assessment
data segments at given points during the MOOC to computing device
120 for presentation to the user via MOOC interface 124. In an
example embodiment, assessment engine 136 may also receive data
from assessment retriever 132 and PCBE 134. The operations and
functions of assessment engine 136 are described in further detail
below with regard to FIG. 2. In another embodiment, the operation
and functionality of PCBE 134 may be performed by assessment engine
136.
[0029] In embodiments of the invention, assessment engine 136 may
perform the action of controlling a transmission of one or more
assessment data segments to the user based on a perception state of
the user determined using sensor data of the user captured by one
or more biometric sensors and further based on profile information
of the user. Controlling the transmission may include skipping at
least one of the one or more assessment data segments based on the
perception state of the user. Controlling the transmission may also
include selecting at least one assessment data segment of the one
or more assessment data segments, from a database, for transmission
to the user, based on the perception state of the user and
transmitting the at least one assessment data segment to the user
on the computing device.
[0030] FIG. 2 shows a flowchart illustrating the operations of
assessment engine 136 in accordance with an example embodiment of
the invention. Referring to step S210, device assessment engine 136
may determine if an assessment data segment of the MOOC is
imminent. In an example embodiment, assessment engine may make this
determination by accessing data captured by assessment retriever
132 such as the timing/scheduling of any pre-designed and
pre-populated assessment data segments of the MOOC. In another
embodiment, assessment engine 136 may, at step S210 receive a
notification of an imminent assessment data segment from assessment
retriever 132. In yet another embodiment, assessment engine 136 may
make this determination by accessing data stored in MOOC database
150, via network 110 and server 130. If it is determined that an
assessment data segment of the MOOC is approaching, assessment
engine 136 may proceed to step S220. In an example embodiment,
assessment engine 136 access the timing/scheduling of pre-designed
assessment data segments of the MOOC captured by assessment
retriever 132 and determines that an assessment data segment of the
MOOC is scheduled to be presented to the individual participating
in the MOOC via computing device 120.
[0031] Referring to step S220, in response to determining that an
assessment data segment of the MOOC is approaching, assessment
engine 136 may retrieve the perception state, as determined by PCBE
engine 134, of the individual for the portion of the MOOC the
approaching assessment data segment is meant to assess. In an
example embodiment, assessment engine 136 may retrieve the
perception state from PCBE engine 134 and/or perception state
mapping database 160. As mentioned above, PCBE 134 may begin
determining perception states of the individual in response to
receiving data from assessment retriever 132 indicating an
approaching assessment of the MOOC. PCBE 134 may also begin
determining perception states of the individual when the individual
starts the MOOC and continue to determine perception states at
various points over the period of time during which the individual
is participating in the MOOC. Also as mentioned above, determined
perception states of an individual over time amount to an evolution
of the individual's perception states, which correspond to
different portions of the MOOC, as he/she progresses through the
MOOC. In an example embodiment, the retrieved perception state of
the individual for the portion of the MOOC the approaching
assessment data segment is meant to assess may indicate that the
individual was bored and on task for that portion of the MOOC.
[0032] Referring to step S230, assessment engine 136 may retrieve
the cognition score and the background match score, as determined
by PCBE engine 134 (described above), of the individual. Assessment
engine 136 may retrieve the cognition score and the background
match score from PCBE engine 134. In an example embodiment,
assessment engine 136 may receive a cognition score and a
background match score which indicates that the individual is
familiar with the portion of the MOOC the approaching assessment
data segment is meant to assess.
[0033] Referring to step S240, assessment engine 136 may compute an
assessment requirement score based on the retrieved perception
state of the individual for the portion of the MOOC the approaching
assessment data segment is meant to assess, the retrieved cognition
score, and the retrieved background match score. For example, the
computed assessment requirement score may be the result of
combining the retrieved perception state, cognition score, and
background match score. In an embodiment of the invention, the
retrieved perception state, cognition score, and background match
score may be weighed equally. In an embodiment of the invention,
the retrieved perception state, cognition score, and background
match score may be weighed differently.
[0034] Referring to step S250, assessment engine 136 may determine
whether or not the computed assessment requirement score is greater
than a threshold value. A Boolean decision may be made, comparing
the assessment requirement score with the threshold value, on
whether an approaching assessment data segment of the MOOC is to be
presented to the individual or not. In embodiments of the
invention, assessment engine 136 may combine the individual's state
of perception, at any point during its evolution, with available
cognition factors (e.g., cognition score and background match
score) for the individual in order to control the transmission of a
particular assessment data segment, for instance, to determine
whether a particular assessment data segment should be transmitted
and presented to the user or skipped (i.e., not transmitted and not
presented). In embodiments of the invention, assessment engine 136
may flag assessment data segments of the MOOC which were skipped.
In an example embodiment where assessment engine 136 determines
that the computed assessment requirement score is not greater than
the threshold value, assessment engine 136 may proceed to step S260
where the approaching data segment of the MOOC is not presented to
the individual and is marked, within MOOC database 150, as being
skipped. Furthermore, assessment engine 136 may transmit, to MOOC
interface 124, the Boolean decision not to present the approaching
assessment data segment of the MOOC to the individual. In an
embodiment where assessment engine 136 determines that the computed
assessment requirement score is greater than the threshold value,
assessment engine 136 may proceed to step S270 where the
approaching data segment of the MOOC is presented to the
individual, via MOOC interface 124 of computing device 120.
[0035] In an alternate embodiment of the invention, assessment
engine 136 may proceed to step S280 in response to determining that
an assessment data segment of the MOOC is not imminent. At step
S280 assessment engine 136 may access PCBE 134 to determine if a
perception state of the individual was flagged by PCBE 134 as being
more difficult for the individual. In yet another alternate
embodiment, assessment engine 136 may receive a notification from
PCBE 134 for every perception state of the individual with was
determined and flagged by PCBE 134 as being more difficult for the
individual. At step S285, assessment engine 136 may determine which
portion, or portions, of the MOOC the individual's difficult
perception state(s) corresponds to. Finally, at step S290,
assessment engine 136 may dynamically generate an assessment data
segment, for transmission to the individual, utilizing questions
from pre-designed assessment data segments of the MOOC meant to
assess the portions of the MOOC which were determined and flagged,
by PCBE 134, as being more difficult for the individual. In doing
so, assessment engine 136 may tailor an assessment data segment to
assess the individual on the portion(s) of the MOOC where he/she
struggled the most, as determined by perception state(s). The
dynamically generated assessment data segment may be transmitted to
the user out of an order specified for pre-designed assessment data
segments of the MOOC.
[0036] FIG. 3 shows a flowchart illustrating further operations of
assessment engine 136 in accordance with an example embodiment of
the invention. Referring to step S310, device assessment engine 136
may determine if the MOOC has completed. In making this
determination, assessment engine 136 may access data stored in MOOC
database 150 which may include digital content of the MOOC and the
timing/scheduling of the digital content. Referring to step S320,
assessment engine 136 may retrieve the cognition score and the
background match score for each assessment data segment taken by
the individual, in response to determining that the MOOC has
completed (i.e., all lecture presentations have been played and all
intermediate assessments have been taken by the individual, via
MOOC interface 124). Referring to step S330, assessment engine 136
may retrieve the perception states of the individual for every
portion of the MOOC the taken assessment data segments were meant
to assess. In steps S320 and S330, assessment engine 136 may access
PCBE 134 and/or perception state mapping database 160 to retrieve
the necessary data. Referring to step S340, assessment engine 136
may retrieve, from MOOC database 150, performance results of the
individual for each assessment data segment of the MOOC taken by
the individual. Referring to step S350, assessment engine 136 may
generate a final assessment data segment for the MOOC containing
questions for the portions of the MOOC where the individual
struggled the most as based on the individual's performance (e.g.,
responses, scores) on earlier assessment data segments of the MOOC,
the retrieved perception states of the individual for the portions
of the MOOC, the retrieved cognition scores for portions of the
MOOC, and the retrieved background match scores for portions of the
MOOC.
[0037] FIG. 4 depicts a block diagram of components of computing
device 120, profiles database 140, MOOC database 150, perception
state mapping database 160, and server 130, in accordance with an
illustrative embodiment of the present invention. It should be
appreciated that FIG. 4 provides only an illustration of one
implementation and does not imply any limitations with regard to
the environments in which different embodiments may be implemented.
Many modifications to the depicted environment may be made.
[0038] Computing device 120, profiles database 140, MOOC database
150, perception state mapping database 160, and server 130 include
communications fabric 902, which provides communications between
computer processor(s) 904, memory 906, persistent storage 908,
network adapter 912, and input/output (I/O) interface(s) 914.
Communications fabric 902 can be implemented with any architecture
designed for passing data and/or control information between
processors (such as microprocessors, communications and network
processors, etc.), system memory, peripheral devices, and any other
hardware components within a system. For example, communications
fabric 902 can be implemented with one or more buses.
[0039] Memory 906 and persistent storage 908 are computer-readable
storage media. In this embodiment, memory 906 includes random
access memory (RAM) 916 and cache memory 918. In general, memory
906 can include any suitable volatile or non-volatile
computer-readable storage media.
[0040] The programs MOOC interface 124 in computing device 120; and
assessment retriever 132, perception-cognition-background engine
134, and assessment engine 136 in server 130 are stored in
persistent storage 908 for execution by one or more of the
respective computer processor(s) 904 via one or more memories of
memory 906. In this embodiment, persistent storage 908 includes a
magnetic hard disk drive. Alternatively, or in addition to a
magnetic hard disk drive, persistent storage 908 can include a
solid state hard drive, a semiconductor storage device, read-only
memory (ROM), erasable programmable read-only memory (EPROM), flash
memory, or any other computer-readable storage media that is
capable of storing program instructions or digital information.
[0041] The media used by persistent storage 908 may also be
removable. For example, a removable hard drive may be used for
persistent storage 908. Other examples include optical and magnetic
disks, thumb drives, and smart cards that are inserted into a drive
for transfer onto another computer-readable storage medium that is
also part of persistent storage 908.
[0042] Network adapter 912, in these examples, provides for
communications with other data processing systems or devices. In
these examples, network adapter 912 includes one or more network
interface cards. Network adapter 912 may provide communications
through the use of either or both physical and wireless
communications links. The programs MOOC interface 124 in computing
device 120; and assessment retriever 132,
perception-cognition-background engine 134, and assessment engine
136 in server 130 may be downloaded to persistent storage 908
through network adapter 912.
[0043] I/O interface(s) 914 allows for input and output of data
with other devices that may be connected to computing device 120,
profiles database 140, MOOC database 150, perception state mapping
database 160, and server 130. For example, I/O interface 914 may
provide a connection to external devices 920 such as a keyboard,
keypad, a touch screen, and/or some other suitable input device.
External devices 920 can also include portable computer-readable
storage media such as, for example, thumb drives, portable optical
or magnetic disks, and memory cards. Software and data used to
practice embodiments of the present invention, e.g., programs MOOC
interface 124 in computing device 120; and assessment retriever
132, perception-cognition-background engine 134, and assessment
engine 136 in server 130, can be stored on such portable
computer-readable storage media and can be loaded onto persistent
storage 908 via I/O interface(s) 914. I/O interface(s) 914 can also
connect to a display 922.
[0044] Display 922 provides a mechanism to display data to a user
and may be, for example, a computer monitor.
[0045] The programs described herein are identified based upon the
application for which they are implemented in a specific embodiment
of the invention. However, it should be appreciated that any
particular program nomenclature herein is used merely for
convenience, and thus the invention should not be limited to use
solely in any specific application identified and/or implied by
such nomenclature.
[0046] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of code, which comprises one or more
executable instructions for implementing the specified logical
function(s). It should also be noted that, in some alternative
implementations, the functions noted in the block may occur out of
the order noted in the figures. For example, two blocks shown in
succession may, in fact, be executed substantially concurrently, or
the blocks may sometimes be executed in the reverse order,
depending upon the functionality involved. It will also be noted
that each block of the block diagrams and/or flowchart
illustration, and combinations of blocks in the block diagrams
and/or flowchart illustration, can be implemented by special
purpose hardware-based systems that perform the specified functions
or acts, or combinations of special purpose hardware and computer
instructions.
[0047] The present invention may be a system, a method, and/or a
computer program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects
of the present invention.
[0048] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0049] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0050] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform aspects of the present
invention.
[0051] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0052] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0053] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0054] While steps of the disclosed method and components of the
disclosed systems and environments have been sequentially or
serially identified using numbers and letters, such numbering or
lettering is not an indication that such steps must be performed in
the order recited and is merely provided to facilitate clear
referencing of the method's steps. Furthermore, steps of the method
may be performed in parallel to perform their described
functionality.
[0055] It is to be understood that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0056] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0057] Characteristics are as follows:
[0058] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0059] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0060] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0061] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0062] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported, providing
transparency for both the provider and consumer of the utilized
service.
[0063] Service Models are as follows:
[0064] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0065] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0066] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0067] Deployment Models are as follows:
[0068] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0069] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0070] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0071] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0072] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure that includes a network of interconnected nodes.
[0073] Referring now to FIG. 5, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 includes one or more cloud computing nodes 100 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 54A, desktop
computer 54B, laptop computer 54C, and/or automobile computer
system 54N may communicate. Nodes 100 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 50 to offer infrastructure,
platforms and/or software as services for which a cloud consumer
does not need to maintain resources on a local computing device. It
is understood that the types of computing devices 54A-N shown in
FIG. 5 are intended to be illustrative only and that computing
nodes 100 and cloud computing environment 50 can communicate with
any type of computerized device over any type of network and/or
network addressable connection (e.g., using a web browser).
[0074] Referring now to FIG. 6, a set of functional abstraction
layers provided by cloud computing environment 50 (FIG. 5) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 6 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0075] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include:
mainframes 61; RISC (Reduced Instruction Set Computer) architecture
based servers 62; servers 63; blade servers 64; storage devices 65;
and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0076] Virtualization layer 70 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 71; virtual storage 72; virtual networks 73,
including virtual private networks; virtual applications and
operating systems 74; and virtual clients 75.
[0077] In one example, management layer 80 may provide the
functions described below. Resource provisioning 81 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 82 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may include application software licenses.
Security provides identity verification for cloud consumers and
tasks, as well as protection for data and other resources. User
portal 83 provides access to the cloud computing environment for
consumers and system administrators. Service level management 84
provides cloud computing resource allocation and management such
that required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 85 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
[0078] Workloads layer 90 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 91; software development and
lifecycle management 92; virtual classroom education delivery 93;
data analytics processing 94; transaction processing 95; and
personalized assessment system 96. Personalized assessment system
96 may relate to providing personalized assessment data segments to
an individual, engaged in a computer-administered course (e.g., a
MOOC), based on the known cognitive state of the individual and
evolution of the state of perception of the individual while
progressing through the course, in addition to profile information
of the individual, if available.
[0079] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. The terminology used herein was chosen to explain the
principles of the one or more embodiments, the practical
application or technical improvement over technologies found in the
marketplace, or to enable others of ordinary skill in the art to
understand the embodiments. Various modifications, additions,
substitutions, and the like will be apparent to those of ordinary
skill in the art without departing from the scope and spirit of the
invention, as defined in the following claims.
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